AI Advantage - The Flywheel

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The data is clear: the AI dividend is now real, but highly uneven. While 96% of organizations investing in AI report productivity gains (EY), only 5% are capturing AI value at scale. These "future-built" firms are already realizing 1.7× higher revenue growth and 3.6× greater total shareholder return than their peers (BCG). 

This advantage compounds through three reinforcing forces. First, the Data Moat: leaders treat data governance as strategic infrastructure that produces proprietary, high-quality data to continuously improve models. Second, the Integration Mandate: rather than treating AI as a cost-cutting tool, leaders reinvest early gains back into enterprise-wide workflows, expanding existing AI capabilities (47%) and developing new ones (42%) (EY). Third, Generative AI as the Precursor to Physical AI: the organizational discipline, data readiness and integration built today form the foundation for the next wave of transformation in robotics and digital twins. 

For late movers, the gap is widening quickly. Many are constrained by a Budget Expectation Gap, where ambition outpaces execution, and are increasingly unprepared for the shift toward Agentic AI, which already represents 17% of total AI value and is poised to fundamentally reshape how work gets done (BCG). Delay is no longer about missed efficiencies; it is about failing to build the competitive engine required to capture the value of the next decade.

The AI Flywheel: Delay is not a strategy

Three years into the GenAI era, most companies have tried AI. Far fewer have wired it into how work actually gets done. The difference between experimentation and integrating is where a new kind of competitive edge is forming. 

The artificial intelligence gold rush of the past few years is now giving way to the high-stakes age of return on investment. The initial frenzy of experimentation is coalescing into a defining competitive dynamic, one that compounds over time.

That dynamic is the Generative AI Flywheel.

Early adopters don't just get linear productivity boosts. They build a flywheel where usage intensity, workflow redesign and reinvestment reinforce one another, creating accelerating returns. Each turn of the flywheel makes the organization faster, smarter and harder to catch. The performance gap is widening quickly, but it's still possible to respond if the focus shifts from tools to operating model.

Research from OpenAI, EY, McKinsey, BCG and others points to the same conclusion: AI value is now real, measurable and highly disproportionate. This is no longer a technology conversation; it is a strategic one. 

The AI dividend is real, but unevenly distributed

OpenAI's enterprise research highlights a meaningful divide in how people use AI, not just whether they use it. Their "frontier" users (roughly the highest-intensity adopters) generate usage that is multiples more than the average employee, and the gap is largest in complex work like analysis and coding. 

That distinction matters because complex workflows are where compounding starts. 

When AI becomes part of multi-step work (planning, analysis, synthesis, execution), it stops being a "time saver" and becomes a capability amplifier. OpenAI reports that enterprise usage is moving beyond basic chat into reusable, repeatable workflows. AI leaders are building "organizational muscle memory," not just individual shortcuts. (1)

The broader market data supports this shift from promise to payoff. EY's AI Pulse Survey shows that among organizations actively investing in AI, 96% report productivity gains, and 57% describe those gains as significant. While attribution remains difficult in complex organizations, a clear pattern emerges: larger, more committed investments correlate with greater impact. (2)

Critically, these gains often require spending beyond traditional "tool budgets," including:

• Data access and quality
• Security and governance
• Integration into systems of record
• Change management
• Training
• Workflow redesign

And this is where the gap widens.

Organizations investing $10M or more in AI are far more likely to report significant productivity gains (71%) than those investing less (52%). (2) At the extreme end, only 5% of companies globally are achieving AI value at scale. These "future-built" firms generate 1.7× higher revenue growth and 3.6× higher total shareholder return than their peers. (6)

As EY notes, the real breakthrough isn't automation, it's amplification. Leaders are scaling human capacity at a pace we've never seen before...". The leaders are not just saving money; they are building a durable competitive advantage. (2)

The three compounding forces of the AI Flywheel

What separates leaders from laggards is not access to technology, it's momentum. Leading organizations are creating a self-reinforcing AI Flywheel driven by three compounding forces.

1. The Integration Mandate

Winning organizations treat AI as an enterprise transformation, not a collection of siloed tools. They aggressively reinvest their initial productivity gains back into their core business to accelerate the next cycle of growth.

Among AI leaders who have already realized AI gains, reinvestment priorities are clear: 

• Expanding existing AI capabilities (47%). (2)
• Developing new AI capabilities (42%). (2)
• Strengthening cybersecurity (41%). (2)

This creates an "Integration Mandate" where productivity gains are immediately channeled back into enterprise workflows (like R&D, innovation, and workforce upskilling) rather than isolated efficiency plays.

This flywheel unfolds in predictable steps: 

Flywheel step A: Skill exposes higher-value use cases.  
Once teams move beyond "draft an email," they start targeting:

• Decision support,
• Risk reduction,
• Revenue growth,
• New capabilities.

Time savings may open the door, but strategic ROI comes from what follows.

Flywheel step B: Higher-value use cases force workflow redesign
This is where AI shifts from feature to infrastructure. Computing does not occur without changing how work flows across people, systems and data. Successful AI-enabled process transformation requires reimagining workflows and redefining responsibilities. (7)

Flywheel step C: Redesign unlocks reinvestment and speed
Gains are converted into additional capability: more integrations, better data, deeper automation, faster experimentation, more reuse. Leaders begin shipping value faster than peers can even learn.  

This is the emerging computing advantage: an organization's ability to convert compute into momentum.

2. The Data Moat

Leading organizations treat data governance not as compliance overhead, but as strategic infrastructure. Every AI-enabled workflow produces refined, proprietary data that improves future performance, creating a 'Data Moat' that laggards struggle to cross.

This discipline is maturing alongside responsible AI practices. EY reports that 68% of senior leaders expect their focus on ethical AI to increase over the next year, reinforcing trust as a prerequisite for scale. (2)

The compounding loop is straightforward:

• Better data access → more trustworthy outputs
• More trustworthy outputs → greater adoption
• More adoption → deeper integration
• More integration → better data instrumentation

And the loop accelerates.  

AI doesn't scale on ambition. It scales on data reliability and integration.

3. The Precursor to Physical AI

The World Economic Forum continues to position robotics and automation as a major transformative force for organizations in the near term. (5)  The organizational readiness — built through Generative AI, data pipelines, governance and workflow integration — forms the essential precursor to Physical AI, the next advanced wave of systems such as robotics and digital twins.

Menlo Ventures' 2025 enterprise report reinforces this trajectory. While GenAI is scaling faster than any software category in history, true agentic deployments remain relatively rare. Most production systems are still simpler than the hype suggests. (3)

This should be encouraging. The window to get serious remains open, but the easy wins phase is ending. The next phase requires infrastructure.

Physical AI is far less forgiving than text. Mistakes cost money, uptime and sometimes safety. Organizations that build AI discipline today will transition faster and safer tomorrow. Those that delay will face a steeper, riskier climb.

The call to action: The rapidly widening performance gap

For the laggards, the reality is a rapidly widening performance gap. and the challenge is not awareness, it's execution.

Many are trapped by the Budget Expectation Gap. A year ago, 34% of senior leaders expected to spend $10 million or more on AI; today, only 23% report actually doing so. Ambition is outpacing commitment. (2) 

At the same time, Agentic AI is accelerating the divide. These advanced systems, which can learn, reason and act autonomously, are quickly becoming a key driver of the competitive chasm. They already account for 17% of AI value today and are projected to reach 29% by 2028. (2)

When we talk about "laggards," it's not a judgment. Most organizations are still early in integration. The risk isn't that you didn't sprint in 2023; it's staying stuck in isolated pilots while others industrialize.

As MIT-linked research consistently shows, pilots fail not because models don't work, but because workflows aren't redesigned around them. To stop the gap from widening, organizations must choose real workflows, connect them to real systems and data, and set real adoption expectations. (4)

Conclusion: Build your competitive engine now

The AI race may feel like a gold rush, but enduring value isn't found in the initial scramble; it's created by the systems that continue extracting value long after others move on.   

Delay is no longer just lost efficiency; it is a failure to build the competitive engine required for the next decade. 

The path to success is clearly delineated, but it requires a fundamental shift in strategy and execution.

• Where will you reinvest the productivity gains AI unlocks?
• Are you renovating workflows, or reimagining business models?
• How do you move beyond productivity and index toward growth?

(1) The state of enterprise AI | OpenAI
(2) AI survey: How AI is turning promise into payoff | EY - US
(3) 2025: The State of Generative AI in the Enterprise | Menlo Ventures
(4) v0.1_State_of_AI_in_Business_2025_Report.pdf
(5) The Future of Jobs Report 2025 | World Economic Forum
(6) Are You Generating Value from AI? The Widening Gap | BCG
(7) Generative AI has ignited a wave of enthusiasm and investment. | McKinsey